Upper bounds for spatial point process approximations

dc.creatorSchuhmacher, Dominic
dc.date2005-03-23
dc.date.accessioned2026-07-07T05:18:16Z
dc.date.available2026-07-07T05:18:16Z
dc.descriptionWe consider the behavior of spatial point processes when subjected to a class of linear transformations indexed by a variable T. It was shown in Ellis [Adv. in Appl. Probab. 18 (1986) 646-659] that, under mild assumptions, the transformed processes behave approximately like Poisson processes for large T. In this article, under very similar assumptions, explicit upper bounds are given for the d_2-distance between the corresponding point process distributions. A number of related results, and applications to kernel density estimation and long range dependence testing are also presented. The main results are proved by applying a generalized Stein-Chen method to discretized versions of the point processes.
dc.descriptionPublished at http://dx.doi.org/10.1214/105051604000000684 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0503491
dc.identifierhttp://arxiv.org/abs/math/0503491
dc.identifierAnnals of Applied Probability 2005, Vol. 15, No. 1B, 615-651
dc.identifierdoi:10.1214/105051604000000684
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/74603
dc.subjectProbability
dc.subject60G55 (Primary) 62E20, 62G07. (Secondary)
dc.titleUpper bounds for spatial point process approximations
dc.typetext

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